# Copyright 2021 Yan Yan # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import time from pathlib import Path import numpy as np import torch from torch import nn from cumm import tensorview as tv import spconv.pytorch as spconv from spconv.utils import Point2VoxelCPU3d def waymo_data(batch_size=1): gen = Point2VoxelCPU3d([0.1, 0.1, 0.1], [-80, -80, -2, 80, 80, 6], 3, 150000, 1) # gen = VoxelGeneratorV2([0.1, 0.1, 0.1], [-80, -80, -2, 80, 80, 6], 1, # 150000) data = np.load(Path(__file__).parent / "data" / "benchmark-pc.npz") pc = np.ascontiguousarray(data["pc"]) print(pc.shape) voxels_tv, indices_tv, _ = gen.point_to_voxel(tv.from_numpy(pc)) voxels = voxels_tv.numpy().reshape(-1, 3) coors = indices_tv.numpy() N = coors.shape[0] coors = np.concatenate([np.full([N, 1], 0, coors.dtype), coors], axis=1) return voxels, coors, gen.grid_size class Net(nn.Module): def __init__(self, shape, algo): super().__init__() self.net = spconv.SparseSequential( spconv.SubMConv3d(3, 64, 3, bias=False, indice_key="c0", algo=algo), # spconv.SubMConv3d(32, # 32, # 3, # bias=False, # indice_key="c0", # algo=algo), # # nn.BatchNorm1d(32), # # nn.ReLU(), # # spconv.SparseConv3d(64, 64, 2, 2, bias=False, # # algo=algo), # spconv.SubMConv3d(32, 64, 3, bias=False, indice_key="c0", # algo=algo), spconv.SubMConv3d(64, 64, 3, bias=False, indice_key="c0", algo=algo), # nn.BatchNorm1d(32), # nn.ReLU(), spconv.SparseConv3d(64, 64, 2, 2, bias=False, indice_key="m0"), # spconv.SparseMaxPool3d(2, 2), spconv.SubMConv3d(64, 96, 3, bias=False, indice_key="c1", algo=algo), spconv.SubMConv3d(96, 96, 3, bias=False, indice_key="c1", algo=algo), # nn.BatchNorm1d(64), # nn.ReLU(), spconv.SparseConv3d(96, 96, 2, 2, bias=False, indice_key="m1"), # spconv.SparseMaxPool3d(2, 2), spconv.SubMConv3d(96, 128, 3, bias=False, indice_key="c2", algo=algo), spconv.SubMConv3d(128, 128, 3, bias=False, indice_key="c2", algo=algo), # nn.BatchNorm1d(128), # nn.ReLU(), spconv.SparseConv3d(128, 128, 2, 2, bias=False, indice_key="m2"), # spconv.SparseMaxPool3d(2, 2), spconv.SubMConv3d(128, 160, 3, bias=False, indice_key="c3", algo=algo), spconv.SubMConv3d(160, 160, 3, bias=False, indice_key="c3", algo=algo), # nn.BatchNorm1d(128), # nn.ReLU(), spconv.SparseConv3d(160, 160, 2, 2, bias=False, indice_key="m3"), # spconv.SparseMaxPool3d(2, 2), spconv.SubMConv3d(160, 192, 3, bias=False, indice_key="c4", algo=algo), spconv.SubMConv3d(192, 192, 3, bias=False, indice_key="c4", algo=algo), # nn.BatchNorm1d(128), # nn.ReLU(), # spconv.SparseMaxPool3d(2, 2, indice_key="m4"), spconv.SparseConv3d(192, 192, 2, 2, bias=False, indice_key="m4"), spconv.SubMConv3d(192, 224, 3, bias=False, indice_key="c5", algo=algo), spconv.SubMConv3d(224, 224, 3, bias=False, indice_key="c5", algo=algo), nn.BatchNorm1d(224), nn.ReLU(), spconv.SparseConv3d(224, 224, 2, 2, bias=False, indice_key="m5"), # spconv.SparseMaxPool3d(2, 2, indice_key="m5"), spconv.SubMConv3d(224, 256, 3, bias=False, indice_key="c6", algo=algo), spconv.SubMConv3d(256, 256, 3, bias=False, indice_key="c6", algo=algo), nn.BatchNorm1d(256), nn.ReLU(), spconv.SparseInverseConv3d(256, 128, 2, indice_key="m5", bias=False), nn.BatchNorm1d(128), nn.ReLU(), spconv.SparseInverseConv3d(128, 64, 2, indice_key="m4", bias=False), ) max_batch_size = 1 # grid (dense map) is used for indice generation. use pre-allocated grid can run faster. self.grid = torch.full([max_batch_size, *shape], -1, dtype=torch.int32).cuda() # self.grid = None self.shape = shape def forward(self, features, coors, batch_size): x = spconv.SparseConvTensor(features, coors, self.shape, batch_size, self.grid) return self.net(x) class Net2(nn.Module): def __init__(self, shape, algo): super().__init__() self.net = spconv.SparseSequential( spconv.SubMConv3d(3, 128, 3, bias=False, indice_key="c0", algo=algo), # spconv.SubMConv3d(32, # 32, # 3, # bias=False, # indice_key="c0", # algo=algo), # # nn.BatchNorm1d(32), # # nn.ReLU(), # # spconv.SparseConv3d(64, 64, 2, 2, bias=False, # # algo=algo), # spconv.SubMConv3d(32, 64, 3, bias=False, indice_key="c0", # algo=algo), spconv.SubMConv3d(128, 128, 3, bias=False, indice_key="c0", algo=algo), # nn.BatchNorm1d(32), # nn.ReLU(), # spconv.SparseMaxPool3d(2, 2), # spconv.SubMConv3d(256, # 512, # 3, # bias=False, # indice_key="c1", # algo=algo), # spconv.SubMConv3d(512, # 512, # 3, # bias=False, # indice_key="c1", # algo=algo), ) max_batch_size = 1 # grid (dense map) is used for indice generation. use pre-allocated grid can run faster. self.grid = torch.full([max_batch_size, *shape], -1, dtype=torch.int32).cuda() # self.grid = None self.shape = shape def forward(self, features, coors, batch_size): x = spconv.SparseConvTensor(features, coors, self.shape, batch_size, self.grid) return self.net(x) def main(): import pickle np.random.seed(50051) torch.manual_seed(50051) # voxels, coors, spatial_shape = waymo_data() # with open("/home/yy/test_spconv.pkl", "wb") as f: # pickle.dump((voxels, coors, spatial_shape), f) with open(Path(__file__).parent / "data" / "test_spconv.pkl", "rb") as f: (voxels, coors, spatial_shape) = pickle.load(f) print(spatial_shape) print(voxels.shape) # voxels = voxels[:100] # coors = coors[:100] dtype = torch.float32 voxels_th = torch.from_numpy(voxels).cuda().to(dtype) coors_th = torch.from_numpy(coors).cuda().int() voxels_th.requires_grad = True algo = spconv.ConvAlgo.Native net = Net(spatial_shape, algo).cuda().eval().to(dtype) print(coors_th.shape) out = net(voxels_th, coors_th, 1) print(out.spatial_shape) print(voxels.mean(), voxels.max(), voxels.min()) dout = np.random.uniform(-0.2, 0.2, out.features.shape).astype(np.float32) dout_t = torch.from_numpy(dout).cuda().to(dtype) print(out.spatial_shape, out.features.mean(), out.features.max(), out.features.min()) # times = [] # with torch.no_grad(): # for i in range(20): # print("------------") # torch.cuda.synchronize() # t = time.time() # out_nograd = net(voxels_th, coors_th, 1) # torch.cuda.synchronize() # times.append(time.time() - t) # print("spconv time", np.mean(times[10:])) times = [] for i in range(1): out = net(voxels_th, coors_th, 1) print("------------") torch.cuda.synchronize() t = time.time() out.features.backward(dout_t) torch.cuda.synchronize() times.append(time.time() - t) # # print((net.grid == -1).float().sum(), net.grid.numel()) # # print("spconv time", time.time() - t) # print("spconv bw time", np.mean(times[5:])) if __name__ == "__main__": main()